OpenRaiser/Intern-Atlas huggingface dataset:
Question Generation by Transformers
Venue: arXiv cs.CL 2019
Year: 2019

A machine learning model was developed to automatically generate questions from Wikipedia passages using transformers, an attention-based model eschewing the paradigm of existing recurrent neural networks (RNNs). The model was trained on the inverted Stanford Question Answering Dataset (SQuAD), which is a reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles. After training, the question generation model is able to generate simple questions relevant to unseen passages and answers containing an average of 8 words per question. The word error rate (WER) was used as a metric to compare the similarity between SQuAD questions and the model-generated questions. Although the high average WER suggests that the questions generated differ from the original SQuAD questions, the questions generated are mostly grammatically correct and plausible in their own right.
Local block-wise self attention for normal organ segmentation
Venue: arXiv cs.CV 2019
Year: 2019

We developed a new and computationally simple local block-wise self attention based normal structures segmentation approach applied to head and neck computed tomography (CT) images. Our method uses the insight that normal organs exhibit regularity in their spatial location and inter-relation within images, which can be leveraged to simplify the computations required to aggregate feature information. We accomplish this by using local self attention blocks that pass information between each other to derive the attention map. We show that adding additional attention layers increases the contextual field and captures focused attention from relevant structures. We developed our approach using U-net and compared it against multiple state-of-the-art self attention methods. All models were trained on 48 internal headneck CT scans and tested on 48 CT scans from the external public domain database of computational anatomy dataset. Our method achieved the highest Dice similarity coefficient segmentation accuracy of 0.85$\pm$0.04, 0.86$\pm$0.04 for left and right parotid glands, 0.79$\pm$0.07 and 0.77$\pm$0.05 for left and right submandibular glands, 0.93$\pm$0.01 for mandible and 0.88$\pm$0.02 for the brain stem with the lowest increase of 66.7\% computing time per image and 0.15\% increase in model parameters compared with standard U-net. The best state-of-the-art method called point-wise spatial attention, achieved \textcolor{black}{comparable accuracy but with 516.7\% increase in computing time and 8.14\% increase in parameters compared with standard U-net.} Finally, we performed ablation tests and studied the impact of attention block size, overlap of the attention blocks, additional attention layers, and attention block placement on segmentation performance.
Good-for-MDPs Automata for Probabilistic Analysis and Reinforcement Learning
Venue: arXiv cs.FL 2019
Year: 2019

We characterize the class of nondeterministic $ω$-automata that can be used for the analysis of finite Markov decision processes (MDPs). We call these automata `good-for-MDPs' (GFM). We show that GFM automata are closed under classic simulation as well as under more powerful simulation relations that leverage properties of optimal control strategies for MDPs. This closure enables us to exploit state-space reduction techniques, such as those based on direct and delayed simulation, that guarantee simulation equivalence. We demonstrate the promise of GFM automata by defining a new class of automata with favorable properties - they are Büchi automata with low branching degree obtained through a simple construction - and show that going beyond limit-deterministic automata may significantly benefit reinforcement learning.
Appearance invariant Entry-Exit matching using visual soft biometric traits
Venue: arXiv cs.CV 2019
Year: 2019

The problem of appearance invariant subject recognition for Entry-Exit surveillance applications is addressed. A novel Semantic Entry-Exit matching model that makes use of ancillary information about subjects such as height, build, complexion and clothing color to endorse exit of every subject who had entered private area is proposed in this paper. The proposed method is robust to variations in clothing. Each describing attribute is given equal weight while computing the matching score and hence the proposed model achieves high rank-k accuracy on benchmark datasets. The soft biometric traits used as a combination though cannot achieve high rank-1 accuracy, it helps to narrow down the search to match using reliable biometric traits such as gait and face whose learning and matching time is costlier when compared to the visual soft biometrics.
Context Aware Road-user Importance Estimation (iCARE)
Venue: arXiv cs.CV 2019
Year: 2019

Road-users are a critical part of decision-making for both self-driving cars and driver assistance systems. Some road-users, however, are more important for decision-making than others because of their respective intentions, ego vehicle's intention and their effects on each other. In this paper, we propose a novel architecture for road-user importance estimation which takes advantage of the local and global context of the scene. For local context, the model exploits the appearance of the road users (which captures orientation, intention, etc.) and their location relative to ego-vehicle. The global context in our model is defined based on the feature map of the convolutional layer of the module which predicts the future path of the ego-vehicle and contains rich global information of the scene (e.g., infrastructure, road lanes, etc.), as well as the ego vehicle's intention information. Moreover, this paper introduces a new data set of real-world driving, concentrated around inter-sections and includes annotations of important road users. Systematic evaluations of our proposed method against several baselines show promising results.
On Memory Mechanism in Multi-Agent Reinforcement Learning
Venue: arXiv cs.MA 2019
Year: 2019

Multi-agent reinforcement learning (MARL) extends (single-agent) reinforcement learning (RL) by introducing additional agents and (potentially) partial observability of the environment. Consequently, algorithms for solving MARL problems incorporate various extensions beyond traditional RL methods, such as a learned communication protocol between cooperative agents that enables exchange of private information or adaptive modeling of opponents in competitive settings. One popular algorithmic construct is a memory mechanism such that an agent's decisions can depend not only upon the current state but also upon the history of observed states and actions. In this paper, we study how a memory mechanism can be useful in environments with different properties, such as observability, internality and presence of a communication channel. Using both prior work and new experiments, we show that a memory mechanism is helpful when learning agents need to model other agents and/or when communication is constrained in some way; however we must to be cautious of agents achieving effective memoryfulness through other means.
Self-Attentional Models Application in Task-Oriented Dialogue Generation Systems
Venue: arXiv cs.CL 2019
Year: 2019

Self-attentional models are a new paradigm for sequence modelling tasks which differ from common sequence modelling methods, such as recurrence-based and convolution-based sequence learning, in the way that their architecture is only based on the attention mechanism. Self-attentional models have been used in the creation of the state-of-the-art models in many NLP tasks such as neural machine translation, but their usage has not been explored for the task of training end-to-end task-oriented dialogue generation systems yet. In this study, we apply these models on the three different datasets for training task-oriented chatbots. Our finding shows that self-attentional models can be exploited to create end-to-end task-oriented chatbots which not only achieve higher evaluation scores compared to recurrence-based models, but also do so more efficiently.
Privacy-Aware Cost-Effective Scheduling Considering Non-Schedulable Appliances in Smart Home
Venue: arXiv cs.CR 2019
Year: 2019

A Smart Home provides integrating and electronic information services to help residential users manage their energy usage and bill cost but also exposes users to significant privacy risks due to fine-grained information collected by smart meters. Taking account of users' privacy concerns, this paper focuses on cost-effective runtime scheduling designed for schedulable and non-schedulable appliances. To alleviate the influence of operation uncertainties introduced by non-schedulable appliances, we formulate the problem by minimizing the expected sum of electricity cost under the worst privacy situation. Inventing the iterative alternative algorithm, we effectively schedule the appliances and rechargeable battery in a cost-effective way while satisfying users' privacy requirement. Experimental evaluation based on real-world data demonstrates the effectiveness of the proposed algorithm.
Tracking the untracked
Venue: arXiv cs.HC 2019
Year: 2019

The issue of seamless identification of users previously tracked using existing real-time optical position tracking system such as the OptiTrack system and maintaining continuous tracking state (history) of each of those users is a hard problem. In this article, we present a theoretical framework to integrate existing tracking systems with features such as user identification and history of up to `n' person activity. In our approach, we assume no direct communication with the tracking system, but access to all data it collects. Also, there are no guarantees that 1) the order of each tracked retro-reflective sphere reported is the same, and 2) that there will be any particular number of spheres in the room at any given time. We describe how the data is fused with existing tracking data to provide a seamless transition between other forms of position tracking.
From Textual Information Sources to Linked Data in the Agatha Project
Venue: arXiv cs.CL 2019
Year: 2019

Automatic reasoning about textual information is a challenging task in modern Natural Language Processing (NLP) systems. In this work we describe our proposal for representing and reasoning about Portuguese documents by means of Linked Data like ontologies and thesauri. Our approach resorts to a specialized pipeline of natural language processing (part-of-speech tagger, named entity recognition, semantic role labeling) to populate an ontology for the domain of criminal investigations. The provided architecture and ontology are language independent. Although some of the NLP modules are language dependent, they can be built using adequate AI methodologies.
Minimum firing times of firing squad synchronization problems for paths in grid spaces
Venue: arXiv cs.FL 2019
Year: 2019

We consider the firing squad synchronization problems for paths in the two and the three-dimensional grid spaces. Minimal-time solutions of these problems are not known and are unlikely to exist. However, at present we have no proofs of their nonexistence. In this paper we show one result that suggests what type of study is necessary in order to prove their nonexistence.
The firing squad synchronization problem for squares with holes
Venue: arXiv cs.FL 2019
Year: 2019

The firing squad synchronization problem (FSSP, for short) is a problem in automata theory introduced in 1957 by John Myhill. Its goal is to design a finite automaton A such that, if copies of A are placed in a line and connected and are started at time 0 with their leftmost copy in a special triggering state, then at some time (the "firing time") all copies enter a special "firing state" simultaneously for the first time. FSSP has many variations and for many of them we know minimal-time solutions (solutions having shortest firing time). One of such variations is the FSSP for squares (denoted by SQ) in which copies are placed in a square. In this paper we introduce a variation which we call the FSSP for squares with k holes and denote by SH[k] by slightly modifying SQ (k >= 1). In the variation, copies of a finite automaton are placed in a square but there are k positions ("holes") in the square where no copies are placed. We show that SH[1] has a minimal-time solution. Moreover, for each problem instance (a placement of copies in a square) C of SH[2], we determine the minimum firing time of C (the minimum value of firing times of C by A where A ranges over all solutions of SH[2]). The variation SQ was introduced and its minimal-time solutions were found in 1970's. However, to find minimal-time solutions of SH[k], a very simple modification of SQ, seems to be a very difficult and challenging problem for k >= 2.
Further Applications of Wireless Communication Systems over $α-η-κ-μ$ Fading Channels
Venue: arXiv cs.IT 2019
Year: 2019

In this letter, some applications of wireless communication systems over $α-η-κ-μ$ fading channels are analysed. More specifically, the effective rate and the average of both the detection probability and area under the receiver characteristics curve of energy detection based spectrum sensing are studied. Furthermore, highly accurate method to truncating the infinite summation of the probability density function of $α-η-κ-μ$ fading conditions for a certain number of terms is provided. To this end, novel mathematically tractable exact expressions are derived in terms of the Fox's $H$-function (FHF). The asymptotic behaviour is also explained to earn further insights into the effect of the fading parameters on the system performance. A comparison between the numerical results and Monte Carlo simulations is presented to verify the validation of our analysis.
CUNI System for the Building Educational Applications 2019 Shared Task: Grammatical Error Correction
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we describe our systems submitted to the Building Educational Applications (BEA) 2019 Shared Task (Bryant et al., 2019). We participated in all three tracks. Our models are NMT systems based on the Transformer model, which we improve by incorporating several enhancements: applying dropout to whole source and target words, weighting target subwords, averaging model checkpoints, and using the trained model iteratively for correcting the intermediate translations. The system in the Restricted Track is trained on the provided corpora with oversampled "cleaner" sentences and reaches 59.39 F0.5 score on the test set. The system in the Low-Resource Track is trained from Wikipedia revision histories and reaches 44.13 F0.5 score. Finally, we finetune the system from the Low-Resource Track on restricted data and achieve 64.55 F0.5 score, placing third in the Unrestricted Track.
On the Effect of Observed Subject Biases in Apparent Personality Analysis from Audio-visual Signals
Venue: arXiv cs.CV 2019
Year: 2019

Personality perception is implicitly biased due to many subjective factors, such as cultural, social, contextual, gender and appearance. Approaches developed for automatic personality perception are not expected to predict the real personality of the target, but the personality external observers attributed to it. Hence, they have to deal with human bias, inherently transferred to the training data. However, bias analysis in personality computing is an almost unexplored area. In this work, we study different possible sources of bias affecting personality perception, including emotions from facial expressions, attractiveness, age, gender, and ethnicity, as well as their influence on prediction ability for apparent personality estimation. To this end, we propose a multi-modal deep neural network that combines raw audio and visual information alongside predictions of attribute-specific models to regress apparent personality. We also analyse spatio-temporal aggregation schemes and the effect of different time intervals on first impressions. We base our study on the ChaLearn First Impressions dataset, consisting of one-person conversational videos. Our model shows state-of-the-art results regressing apparent personality based on the Big-Five model. Furthermore, given the interpretability nature of our network design, we provide an incremental analysis on the impact of each possible source of bias on final network predictions.
Fully Anonymous Shared Memory Algorithms
Venue: arXiv cs.DC 2019
Year: 2019

Process anonymity has been studied for a long time. Memory anonymity is more recent. In an anonymous memory system, there is no a priori agreement among the processes on the names of the shared registers they access. This article introduces the fully anonymous model, namely a model in which both the processes and the memory are anonymous. It is shown that fundamental problems such as mutual exclusion, consensus, and its weak version called set agreement, can be solved despite full anonymity, the first in a failure-free system, the others in the presence of any number of process crashes.
Automated Detecting and Placing Road Objects from Street-level Images
Venue: arXiv cs.CV 2019
Year: 2019

Navigation services utilized by autonomous vehicles or ordinary users require the availability of detailed information about road-related objects and their geolocations, especially at road intersections. However, these road intersections are mainly represented as point elements without detailed information, or are even not available in current versions of crowdsourced mapping databases including OpenStreetMap(OSM). This study develops an approach to automatically detect road objects and place them to right location from street-level images. Our processing pipeline relies on two convolutional neural networks: the first segments the images, while the second detects and classifies the specific objects. Moreover, to locate the detected objects, we establish an attributed topological binary tree(ATBT) based on urban grammar for each image to depict the coherent relations of topologies, attributes and semantics of the road objects. Then the ATBT is further matched with map features on OSM to determine the right placed location. The proposed method has been applied to a case study in Berlin, Germany. We validate the effectiveness of our method on two object classes: traffic signs and traffic lights. Experimental results demonstrate that the proposed approach provides near-precise localization results in terms of completeness and positional accuracy. Among many potential applications, the output may be combined with other sources of data to guide autonomous vehicles
A Channel-Pruned and Weight-Binarized Convolutional Neural Network for Keyword Spotting
Venue: arXiv cs.LG 2019
Year: 2019

We study channel number reduction in combination with weight binarization (1-bit weight precision) to trim a convolutional neural network for a keyword spotting (classification) task. We adopt a group-wise splitting method based on the group Lasso penalty to achieve over 50% channel sparsity while maintaining the network performance within 0.25% accuracy loss. We show an effective three-stage procedure to balance accuracy and sparsity in network training.
An Objectness Score for Accurate and Fast Detection during Navigation
Venue: arXiv cs.CV 2019
Year: 2019

We propose a novel method utilizing an objectness score for maintaining the locations and classes of objects detected from Mask R-CNN during mobile robot navigation. The objectness score is defined to measure how well the detector identifies the locations and classes of objects during navigation. Specifically, it is designed to increase when there is sufficient distance between a detected object and the camera. During the navigation process, we transform the locations of objects in 3D world coordinates into 2D image coordinates through an affine projection and decide whether to retain the classes of detected objects using the objectness score. We conducted experiments to determine how well the locations and classes of detected objects are maintained at various angles and positions. Experimental results showed that our approach is efficient and robust, regardless of changing angles and distances.
DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation
Venue: arXiv cs.CV 2019
Year: 2019

Estimating the travel time for a given path is a fundamental problem in many urban transportation systems. However, prior works fail to well capture moving behaviors embedded in paths and thus do not estimate the travel time accurately. To fill in this gap, in this work, we propose a novel neural network framework, namely {\em Deep Image-based Spatio-Temporal network (DeepIST)}, for travel time estimation of a given path. The novelty of DeepIST lies in the following aspects: 1) we propose to plot a path as a sequence of "generalized images" which include sub-paths along with additional information, such as traffic conditions, road network and traffic signals, in order to harness the power of convolutional neural network model (CNN) on image processing; 2) we design a novel two-dimensional CNN, namely {\em PathCNN}, to extract spatial patterns for lines in images by regularization and adopting multiple pooling methods; and 3) we apply a one-dimensional CNN to capture temporal patterns among the spatial patterns along the paths for the estimation. Empirical results show that DeepIST soundly outperforms the state-of-the-art travel time estimation models by 24.37\% to 25.64\% of mean absolute error (MAE) in multiple large-scale real-world datasets.
A Novel Design of Adaptive and Hierarchical Convolutional Neural Networks using Partial Reconfiguration on FPGA
Venue: arXiv cs.CV 2019
Year: 2019

Nowadays most research in visual recognition using Convolutional Neural Networks (CNNs) follows the "deeper model with deeper confidence" belief to gain a higher recognition accuracy. At the same time, deeper model brings heavier computation. On the other hand, for a large chunk of recognition challenges, a system can classify images correctly using simple models or so-called shallow networks. Moreover, the implementation of CNNs faces with the size, weight, and energy constraints on the embedded devices. In this paper, we implement the adaptive switching between shallow and deep networks to reach the highest throughput on a resource-constrained MPSoC with CPU and FPGA. To this end, we develop and present a novel architecture for the CNNs where a gate makes the decision whether using the deeper model is beneficial or not. Due to resource limitation on FPGA, the idea of partial reconfiguration has been used to accommodate deep CNNs on the FPGA resources. We report experimental results on CIFAR-10, CIFAR-100, and SVHN datasets to validate our approach. Using confidence metric as the decision making factor, only 69.8%, 71.8%, and 43.8% of the computation in the deepest network is done for CIFAR-10, CIFAR-100, and SVHN while it can maintain the desired accuracy with the throughput of around 400 images per second for SVHN dataset.